Biologic Pathway Analysis

最近的生物医学研究在揭示人类疾病的复杂性方面取得了很大进展，而技术突破现在允许对分子行为进行更详细的分析。然而，结果是，在通过单个基因追踪疾病的传统模式中，实验结果往往过于复杂，无法合成。取而代之的是，分子途径作为分析研究的新框架正变得越来越突出。路径整合了整个基因组中的信息，同时反映了真实的生物过程。破坏整个通路的良性行为，而不一定是通路的单个组成部分，可能是疾病的基础。到目前为止，还没有可靠或直接的方法来将大多数基因研究中常见的大规模分子表达数据转化为途径水平上的有意义的数据。为了促进这种有希望的研究模式，病理学家已经发展成为一种能够对分子数据进行系统和有效的以途径为中心的分析的资源。病理学家是一种新的工具，旨在自动分析分子途径背景下的大量遗传数据。该工具旨在以实验室研究人员和信息学分析人员都可访问的形式促进对途径行为的定量和定性分析。最重要的是，病理学家使用核糖核酸表达数据来计算一组500多条典型通路中每条通路的2个描述性指标-活性和一致性(来源：通路相互作用数据库http://pid.nci.nih.gov).活动性分数提供了路径内相互作用发生的可能性的度量，而一致性分数通过比较交互作用的预期和非因素结果来提供路径逻辑的度量。可以为任意数量的样本以及整个路径集合的任何子集生成路径分数。然后，该程序允许通过综合可视化路径组件、结构和分数、路径和样本的分级聚类以及旨在确定路径分数与癌症类型或患者生存等临床特征之间的关联的统计分析来详细探索结果。病理学家提供了一种强大的手段来识别与疾病有关的常见分子过程。通过观察途径水平上的分子行为，病理学家产生的指标通常提供了比单个基于基因的分析更深入的疾病病理见解。该工具已经被用于预测癌症治疗的反应和识别与癌症表型相关的分子特征等多种应用。除了病理学家，比托实验室还使用区分路径分析(PODA)。我们将PODA应用于从癌症基因组易感性标记(CGEMS)乳腺癌研究中获得的2287个基因类型。简而言之，样本包括1145例乳腺癌病例和来自护士健康研究参与者的相当数量的匹配对照组(1142例)。所有参与者都是欧洲血统的美国女性。这些样本使用Illumina 550K阵列进行了基因分型，该阵列分析了全基因组中超过550,000个SNP。为了用观察数据初步评估PodA的有效性，我们首先检查了包含FGFR2内含子2中的四个SNP的SNP集，据报道，这些SNP与病例状态显著相关(59)。正如预期的那样，我们看到了显著的差异。接下来，我们使用CGEMS数据，将PODA系统地应用于以PID(28)表示的通路。数据中总共有69453个SNP可能与至少一条途径相关。观察到这些SNP代表4446个独特的基因，每个基因最显著的SNP被保留以供进一步分析。计算每个路径的病例和对照的Wilcoxon p值，使用FDR调整(60，61)纠正多重假设，并通过重新采样到虚拟路径来重新评估重要路径以调整路径大小。最显著的相关途径是局灶性粘连。有趣的是，这一途径已经成为新型癌症治疗药物的靶点(62-)。FGFR2包括成纤维细胞生长因子信号转导、MAPK信号转导、肌动蛋白细胞骨架调控和前列腺癌四个网络。所有这些都产生了显著的p值，然而，与相同长度的随机产生的通路相比，只有肌动蛋白细胞骨架的调节是显著的。为了评估结果是否完全由于FGFR2的存在，我们从肌动蛋白细胞骨架途径的调控中消除了FGFR2 SNP并重新计算了p值；虽然Wilcoxon检验的p值上升，但它仍然非常显著，这表明肌动蛋白细胞骨架调控与乳腺癌的关联不仅仅是由FGFR2的差异驱动的。

Recent biomedical research has made great progress in unveiling the complexity of human disease, while technological breakthroughs now allow much more detailed analysis of molecular behavior. As a result however, experimental results are frequently too complex for synthesis in the traditional model of tracing disease through individual genes. Instead, molecular pathways are gaining prominence as a new framework for analytic research. Pathways integrate information from across the entire genome while mirroring real biological processes. Disruption of the benign behavior of a pathway as a whole, not necessarily a single component of the pathway, could be the basis for disease. As yet, there exists no robust or straightforward means to transform the large-scale molecular expression data common to most genetic studies into meaningful data at the pathway level. To facilitate this promising mode of investigation, the PathOlogist has been developed as a resource capable of systematic and efficient pathway-centric analysis of molecular data. The PathOlogist is a new tool designed to automatically analyze large sets of genetic data within the context of molecular pathways. The tool aims to facilitate both a quantitative and qualitative analysis of pathway behavior in a format accessible to both laboratory researchers and informatics analysts. Foremost, the PathOlogist uses RNA expression data to calculate 2 descriptive metrics - activity and consistency - for each pathway in a set of more than 500 canonical pathways (source: Pathway Interaction Database http://pid.nci.nih.gov). Activity scores provide ameasure of how likely the interactions within the pathway are to occur while consistencyscores provide a measure of pathway logic by comparing the expected with de factooutcome of interactions. Pathway scores can be generated for any number of samples,and for any subset of the entire pathway collection. The program then allows a detailed exploration of the results through integrated visualization of pathway components, structure, and scores, hierarchical clustering of pathways and samples, and statistical analyses designed to identify associations between pathway scores and clinical features such as cancer type or patient survival. The PathOlogist provides a powerful means of identifying common molecular processes implicated in disease. By viewing molecular behavior at the pathway level, the metrics generated by the PathOlogist often provide further insight into disease pathology than could be gained from individual gene-based analyses. The tool is already being used for such diverse applications as predicting response to cancer treatment and identifying molecular signatures associated with cancer phenotype.In addition to PathOlogist, the Buetow lab uses the Pathways of Distinction Analysis(PoDA). We applied PoDA to 2287 genotypes obtained from the Cancer Genomic Markers of Susceptibility (CGEMS) breast cancer study. Briefly, thesamples comprised 1145 breast cancer cases and a comparable number (1142) of matched controls from the participants of the Nurses Health Study. All the participants were American women of European descent. The samples were genotyped using the Illumina 550K arrays, which assays over 550,000 SNPs across the genome.To provide a preliminary assessment of the validity of PoDA with observational data, we first examined a SNP set comprising the four SNPs in intron 2 of FGFR2 that were reported to show significant association with case status in (59) . As expected, we see a significant difference. Next, we applied PoDA systematically to the pathways represented in PID (28) using CGEMS data. A total of 69453 SNPs in the data could be associated with at least one of the pathways. These SNPs were observed to represent 4446 unique genes and the most significant SNP for each gene was retained for further analysis. The Wilcoxon p-values for cases and controls were computed for each pathway, and the multiple hypotheses were corrected using FDR adjustment (60,61) and significant pathways were reassessed by resampling to dummy pathways to adjust for pathway size. The most significantly associated pathway is Focal adhesion. Interestingly, this pathway is already being targeted by novel cancer therapeutic drugs (62-64). Four networks: FGF signaling, MAPK signaling, regulation of actin cytoskeleton, and prostate cancer contained FGFR2. All yielded significance p-values, however, only regulation of actin cytoskeleton was significant in comparison to randomly generated pathways of the same length. To assess whether the result is solely due to the presence of FGFR2, we eliminated the FGFR2 SNP from the regulation of actin cytoskeleton pathway and recomputed the p values; while the p-value for the Wilcoxon test rose, it remained highly significant, suggesting that the association of actin cytoskeleton regulation with breast cancer is not driven solely by differences in FGFR2.